What question did this study set out to answer?

To predict the Index of Industrial Production for mining, manufacturing, and electricity sub-series.

February 9, 20260 citations

Forecasting Index of Industrial Production Sub-Series Using Statistical and Deep Learning Approach

PPP. Preethi SRS. A. Jyothi Rani VHV. V. Haragopal

Key Points

To predict the Index of Industrial Production for mining, manufacturing, and electricity sub-series.
Analyzed data from April 2012 to September 2022
Used conventional statistical methods and deep learning approaches
Evaluated model performance using MSE, MAPE, and RMSE
RNN model outperformed other models for all three sub-series
Forecasted IIP sub-series from October 2022 to September 2023

Abstract

The Index of Industrial Production (IIP) is a key economic indicator that tracks manufacturing activity across various sectors. This paper aims to predict the IIP for three sub-series—Mining, Manufacturing, and Electricity—using both conventional statistical methods and deep learning approaches, analyzing data from April 2012 to September 2022. Model performance is evaluated by comparing Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE)1 . The results show that the RNN model outperforms other models for all three sub-series, and is used to forecast these sub-series from October 2022 to September 2023.

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